The diagnostically superior information available from data acquired from various imaging systems, especially that provided by multidetector CT (multiple slices acquired per single rotation of the gantry) where acquisition speed and volumetric resolution provide exquisite diagnostic value, enables the detection of potential problems at earlier and more treatable stages. Given the vast quantity of detailed data acquirable from imaging systems, various algorithms must be developed to efficiently and accurately process image data. With the aid of computers, advances in image processing are generally performed on digital or digitized images.
Digital acquisition systems for creating digital images include digital X-ray radiography, computed tomography (“CT”) imaging, magnetic resonance imaging (“MRI”) and nuclear medicine imaging techniques, such as positron emission tomography (“PET”) and single photon emission computed tomography (“SPECT”). Digital images can also be created from analog images by, for example, scanning analog images, such as typical x-rays films, into a digitized form. Further information concerning digital acquisition systems is found in our above-referenced copending application “Graphical User Interface for Display of Anatomical Information”.
Digital images are created from an array of numerical values representing a property (such as a grey scale value or magnetic field strength) associable with an anatomical location referenced by a particular array location. In 2-D digital images, or slice sections, the discrete array locations are termed pixels. Three-dimensional digital images can be constructed from stacked slice sections through various construction techniques known in the art. The 3-D images are made up of discrete volume elements, also referred to as voxels, composed of pixels from the 2-D images. The pixel or voxel properties can be processed to ascertain various properties about the anatomy of a patient associated with such pixels or voxels.
Once in a digital or digitized format, various analytical approaches can be applied to process digital anatomical images and to detect, identify, display and highlight regions of interest (ROI). For example, digitized images can be processed through various techniques, such as segmentation. Segmentation generally involves separating irrelevant objects (for example, the background from the foreground) or extracting anatomical surfaces, structures, or regions of interest from images for the purposes of anatomical identification, diagnosis, evaluation, and volumetric measurements. Segmentation often involves classifying and processing, on a per-pixel basis, pixels of image data on the basis of one or more characteristics associable with a pixel value. For example, a pixel or voxel may be examined to determine whether it is a local maxima or minima based on the intensities of adjacent pixels or voxels.
Once anatomical regions and structures are constructed and evaluated by analyzing pixels and/or voxels, subsequent processing and analysis exploiting regional characteristics and features can be applied to relevant areas, thus improving both accuracy and efficiency of the imaging system. For example, the segmentation of an image into distinct anatomical regions and structures provides perspectives on the spatial relationships between such regions. Segmentation also serves as an essential first stage of other tasks such as visualization and registration for temporal and cross-patient comparisons.
Image registration is a process of alignment of medical imaging data for facilitating comparisons and medical diagnosis. Image registrations of digital images allow doctors to visualize and monitor physiological changes in a patient over time or to keep track of the growth or decline of lesions. For example, image registration enables doctors to identify, compare and determine the growth of a malignant lesion or nodule. A comprehensive survey of existing medical image registration is given in “Medical Imaging Matching—A Review With Classification”, van den Elsen, Pol, and Viergever, IEEE Engineering in Medicine and Biology, Vol. 12, No. 1, pp. 26-39, 1993. This reference is hereby incorporated for all purposes.
Anatomical registration is a difficult problem because anatomical structures vary widely in appearance and location within different patients. Even within the same patient, the passage of time often brings about great variations. Variations with respect to the patient and patient anatomy are generally referred to as intrinsic variations. Intrinsic, or local, variations refer to structural differences between anatomical structures. For example, intrinsic variations result when image acquisition is performed out of phase (i.e., taking the image during a different breath-phase or heart-beat), but are also unavoidable due to differences between patients and changes in patients over time.
The task of mapping an image of a particular anatomical structure to another image is difficult also due to variations between images. For example, variations can occur due to differences in image acquisition environments, differences between patients (for a cross-patient comparison), and/or corporeal changes over time (for a temporal comparison). Variations due to factors external to a patient anatomy are generally referred to as extrinsic variations. Certain extrinsic, or global, variations result from the lack of standard protocols or orientations for image acquisitions. For example, even if attempts are made to take images along a principal axis, the actual axis along which cross-section images are obtained may be at an angle to the principal axis. Additionally, the use of different fields-of-view, dose, and varying patient size all contribute to extrinsic variations.
Most existing medical image registration methods require user interaction and/or are computationally expensive or intensive. One method commonly used for medical image registration is based on the mutual information approach. The mutual information approach is based upon the maximization of mutual information criteria used to select attributes conditioned on prior registrations. Having its origin in information theory, mutual information criteria generally include a numerical value that indicates how well each attribute discriminates a chosen label attribute, i.e., the relatedness of one random variable to another is based upon a measure of the variables' entropies. One example can include two-dimensional gray-scale histograms of image pairs that are used for registration. One drawback of the mutual information approach is that it generally does not account for different types of variations (i.e., extrinsic and intrinsic variations) between images. Another drawback of the mutual information approach is that entire data sets of the image pairs need to be analyzed. Thus, it is very time consuming and inefficient.
Correlation-based approaches involving cross-correlation or cross-covariance of image pixel intensities provide methods of image registration. However, such approaches are generally not accurate for image content and noise that exhibit large variations and differences.
Another existing method, the atlas model approach, requires the use of a pre-determined “atlas model” that characterizes an anatomical structure being registered. In this approach, generic anatomical structures are used as a pattern for the structure being registered. However, since medical scan procedures do not always scan an anatomical structure in its entirety, or in a particular orientation, and since the population being scanned for pathologies is likely to exhibit abnormalities or variations in his/her anatomical structures, the atlas model approach often results in mismatched registrations. Moreover, the atlas model is not effective in tracking lesions that completely appear or disappear from one image set to another.
Thus, it is desirable to provide systems and methods for registering images based on selective sampling that are insensitive to partial scans. It is yet further desirable to provide systems and methods for registering images that support temporal and cross-patient comparisons. Moreover, it is desirable to provide systems and methods for registering images that can reduce computation time through sampling or without always having to analyze an entire data set. On the other hand, it is desirable to provide systems and methods of registration that improve accuracy and resolution by using relevant information from image sets to compute the image registration or fusion. It is further desirable to provide systems and methods for registering images that provide input data for other computations such as volume and size measurements. It is also desirable that the systems and methods provided for registering images support various data acquisition systems, such as CT, PET or SPECT scanning and imaging.
It is an object of the present invention to address and incorporate the above considerations. A further object of the present invention is to provide systems and methods for registering images based on selective sampling that are insensitive to patient anatomical abnormalities or variations. A further object of the present invention is to bring corresponding structures into alignment or otherwise align one image or sequence with a corresponding image or sequence. Additionally, it is also desirable to provide a system and method that can detect and track vanishing and appearing objects, such as lesions, within the context of the larger surrounding anatomy. The present invention provides a system and method that is accurate and displays high levels of physiological detail over the prior art without specially configured equipment.